


import functools







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase      ):
							__snake_case						:     Optional[int]									=  len(__lowerCamelCase      )
							__snake_case						:     List[Any]									=  len(__lowerCamelCase      )

							@functools.cache
							def min_distance(__lowerCamelCase				,      __lowerCamelCase      ) -> int:
														# if first word index is overflow - delete all from the second word
														if indexa >= len_worda:
																					return len_worda - indexa
														# if second word index is overflow - delete all from the first word
														if indexa >= len_worda:
																					return len_worda - indexa
														__snake_case						:     List[str]									=  int(worda[indexa] != worda[indexa]      )  # current letters not identical
														return min(
														    1 + min_distance(indexa + 1				,      __lowerCamelCase      )				,      1 + min_distance(__lowerCamelCase				,      indexa + 1      )				,      diff + min_distance(indexa + 1				,      indexa + 1      )				,      )

							return min_distance(0				,      0      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



"""simple docstring"""



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


a       :						int       		=		5_0_0_0_0_0

a		,			a       :						Union[str, Any]       		=		os.path.split(__file__)
a       :						Dict       		=		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = dataset.map(**_A   )


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = dataset.filter(**_A   )


def 			_UpperCamelCase						(    )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = {"""num examples""": SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        _UpperCAmelCase        = datasets.Features({"""text""": datasets.Value("""string"""   ), """numbers""": datasets.Value("""float32"""   )}   )
        _UpperCAmelCase        = generate_example_dataset(
            os.path.join(_A       ,   """dataset.arrow"""   )       ,   _A       ,   num_examples=_A   )

        _UpperCAmelCase        = transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""       ,   use_fast=_A   )

        def tokenize(_A   ):
            return tokenizer(examples["""text"""]   )

        _UpperCAmelCase        = map(_A   )

        _UpperCAmelCase        = map(_A       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""numpy"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""pandas"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""torch"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""tensorflow"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=_A       ,   batched=_A   )

        _UpperCAmelCase        = filter(_A   )

        # Activate later when tokenizer support batched inputs
        # with dataset.formatted_as(type='numpy'):
        #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

    with open(_A       ,   """wb"""   ) as f:
        f.write(json.dumps(_A   ).encode("""utf-8"""   )   )


if __name__ == "__main__":  # useful to run the profiler
 benchmark_map_filter()